ECE PhD Dissertation Defense: Pu Zhao

PhD Dissertation Defense: Towards Robust Image Classification with Deep learning and Real-Time DNN Inference on Mobile

Pu Zhao

Location: Zoom Link

Abstract:  As the rapidly increasing popularity of deep learning, deep neural networks (DNN) have become the fundamental and essential building blocks in various applications such as image classification and object detection. However, there are two main issues which potentially limit the wide application of DNNs: 1) the robustness of DNN models raises security concerns, and 2) the large computation and storage requirements of DNN models lead to difficulties for its wide deployment on popular yet resource-constrained devices such as mobile phones.
To investigate the DNN robustness, we explore the DNN attack, robustness evaluation and defense. More specifically, for DNN attack, we achieve various attack goals (e.g. adversarial examples and fault sneaking attacks) with different algorithms (e.g. alternating direction method of multipliers (ADMM) and natural gradient descent (NGD) attacks) under various conditions (white-box and black-box attacks). For robustness evaluation, we propose a fast evaluation method to obtain the model perturbation bound such that any model perturbation within the bound does not alter the model classification outputs or incur model mis-behaviors. For the DNN defense, we investigate the defense performance with model connection techniques and successfully mitigate the fault sneaking and backdoor attacks. With a deeper understanding of the DNN robustness, we further explore the deployment problem of DNN models on edge devices with limited resources.
To satisfy the storage and computation limitation on edge devices, we adopt model pruning to remove the redundancy in models, thus reducing the storage and computation during inference. Besides, as some applications have real-time requirements with high inference speed sensitivities such as object detection on autonomous cars, we further try to implement real-time DNN inference for various DNN applications on mobile devices with pruning and compiler optimization. To summary, we mainly investigate the DNN robustness and implement real-time DNN inference on the mobile.